关键词: 二进制蚁狮算法, 种群保护集, 并行迭代, 0-1背包问题, 混合式特征选择, 轴承故障

Abstract: To reduce the feature dimension of rolling bearing fault data set and select the most effective features of data,an Improved Binary Ant Lion Optimizer (IBALO) was proposed,which introduced the mechanism of population protection set to keep the ants with optimized potential into protection set.The group in the protection set was iterated parallelly with the main group in order to enhance the global optimization capability.Then the validity of IBALO was verified by 0-1 knapsack problem.The above of improvements were integrated into the hybrid feature selection model,and applied to UCI standard test databases and rolling bearing fault databases from Case Western Reserve University respectively.The experimental results showed that the recognition accuracy and feature reduction ability of the hybrid feature selection model based on the IBALO were improved significantly.

Key words: binary ant lion optimizer, population protection set, parallel iteration, 0-1 knapsack problem, hybrid feature selection, bearing fault